EmbeddGAN introduces a new GAN framework that replaces the traditional discriminator with an embedding network trained to maximize statistical dependence between embeddings and real/fake labels using Gini distance correlation (gCor). The generator simultaneously minimizes this dependence, encouraging real and generated samples to become indistinguishable in the learned low‑dimensional embedding space. Experiments on MNIST, CIFAR‑10, and CelebA show competitive performance and notably more stable training dynamics compared to established baselines.
arXiv:2412. 08394v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predictions.
arXiv:2508. 00472v2 Announce Type: replace Abstract: The tabular form constitutes the standard way of representing data in relational database systems and spreadsheets.
arXiv:2508. 01725v5 Announce Type: replace Abstract: Recent advances in continuous conditional generative modeling, including Continuous conditional Generative Adversarial Network (CcGAN) and Continuous Conditional Diffusion Model (CCDM), estimate high-dimensional data distributions conditioned on scalar regression labels such as angles, ages, or temperatures.
arXiv:2512.17730v2 Announce Type: replace Abstract: Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specif...
The paper reexamines GAN training by replacing the traditional cross‑entropy discriminator with an upper bound on the Bayes error rate, using the Bayes optimal learning threshold (BOLT) loss. It shows that, with balanced class priors, the resulting adversarial game is equivalent to minimizing total variation between data and generator distributions, and introduces BOLT‑GAN when the discriminator is constrained to be 1‑Lipschitz. Experiments on four image‑generation benchmarks demonstrate that BOLT‑GAN achieves better sample quality and coverage than WGAN‑GP, reducing FID scores on CIFAR‑10 and LSUN Bedroom‑64 within a 20‑epoch budget.
arXiv:2608.24610v1 Announce Type: new Abstract: Generative adversarial networks (GANs) have garnered considerable attention in molecular discovery for their ability to generate novel and high-quality...
arXiv:2509. 24935v3 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning.
NextGAN introduces a new training approach for GANs that regresses the critic’s input gradient onto displacements between paired reference and generated features in frozen representation spaces, effectively adding a gradient‑energy penalty while keeping the generator’s standard update. This method scales to very large pre‑trained image and video generators (up to 14 B parameters) without extra losses or teacher signals, achieving state‑of‑the‑art one‑step FIDs on ImageNet and superior VBench scores for text‑to‑video generation with a single 1.3 B model.
arXiv:2611.07604v1 Announce Type: cross Abstract: Recent advances in diffusion distillation have enabled high-quality image generation in only one or a few network function evaluations (NFEs). Despit...
arXiv:2608. 10096v1 Announce Type: cross Abstract: Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models.
arXiv:2607. 01907v1 Announce Type: cross Abstract: Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unstable.
arXiv:2608. 13256v1 Announce Type: cross Abstract: As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical.